for itrial in xrange(1,args.ntrials+1):
results = {} # one trial of results
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results['seed'])
fmap = get_random_map(alt_mh_expectation, seed=results['seed'])
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info(
"Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
with Timer() as t:
llh_data = find_opt_bfgs(
fmap, template_maker, template_settings['params'],
minimizer_settings, args.save_steps,
normal_hierarchy=hypo_normal, check_octant=check_octant)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
# Store the LLH data
results[hypo_tag] = llh_data
trials += [results]
tprofile.info("stop trial %d"%itrial)
output[data_tag]['trials'] = trials
to_json(output,args.outfile)
results[data_tag] = {}
# 1) get "Asimov" average fiducial template:
asimov_fmap = get_asimov_fmap(pseudo_data_template_maker,
get_values(select_hierarchy(pseudo_data_settings['params'],
normal_hierarchy=data_normal)),
channel=channel)
# 2) find max llh or min chisquare (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
tprofile.info("start optimizer")
tprofile.info("Using %s"%metric_name)
opt_data = find_opt_bfgs(asimov_fmap,template_maker,template_settings['params'],
minimizer_settings,args.save_steps,
normal_hierarchy=hypo_normal, check_octant=args.check_octant,
metric_name=metric_name)
tprofile.info("stop optimizer")
# Store the optimum data
results[data_tag][hypo_tag] = opt_data
# Assemble output dict
output = {'results' : results,
'template_settings' : template_settings,
'minimizer_settings' : minimizer_settings}
if args.pseudo_data_settings is not None:
output['pseudo_data_settings'] = pseudo_data_settings
pseudo_data_settings['params'],
normal_hierarchy=data_normal)),
channel=channel)
# 2) find max llh or min chisquare (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH', True), ('hypo_IMH', False)]:
physics.info("Finding best fit for %s under %s assumption" %
(data_tag, hypo_tag))
tprofile.info("start optimizer")
tprofile.info("Using %s" % metric_name)
opt_data = find_opt_bfgs(asimov_fmap,
template_maker,
template_settings['params'],
minimizer_settings,
args.save_steps,
normal_hierarchy=hypo_normal,
check_octant=args.check_octant,
metric_name=metric_name)
tprofile.info("stop optimizer")
# Store the optimum data
results[data_tag][hypo_tag] = opt_data
# Assemble output dict
output = {
'results': results,
'template_settings': template_settings,
'minimizer_settings': minimizer_settings
}
results = {} # one trial of results
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results['seed'])
# Get random map generated from asimov data (or from data_tag).
fmap = get_random_map(asimov_data, seed=results['seed'])
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info(
"Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
with Timer() as t:
llh_data = find_opt_bfgs(
fmap, template_maker, template_settings['params'],
minimizer_settings, args.save_steps,
normal_hierarchy=hypo_normal, check_octant=check_octant)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
# Store the LLH data
results[hypo_tag] = llh_data
trials += [results]
tprofile.info("stop trial %d"%itrial)
output[data_tag]['trials'] = trials
to_json(output,args.outfile)
print "atm_params: ", atm_params
# Prepare to store all the steps
steps = {key: [] for key in atm_params.keys()}
steps['llh'] = []
# Iterate over the cartesian product, and set fixed parameter to value
for pos in product(*steplist):
pos_dict = dict(list(pos))
print "Running at params-pos dict: ", pos_dict
for k, v in pos_dict.items():
hypo_params[k]['value'] = v
steps[k].append(v)
with Timer() as t:
llh_data = find_opt_bfgs(asimov_data_set,
template_maker,
hypo_params,
minimizer_settings,
args.save_steps,
normal_hierarchy=hypo_normal)
tprofile.info("==> elapsed time for optimizer: %s sec" % t.secs)
steps['llh'].append(llh_data['llh'][-1])
#Store the LLH data
results[data_tag][hypo_tag] = steps
logging.warn("FINISHED. Saving to file: %s" % args.outfile)
to_json(results, args.outfile)
for name, param in sorted(atm_params.items())]
print "steplist: ",steplist
print "atm_params: ",atm_params
# Prepare to store all the steps
steps = {key:[] for key in atm_params.keys()}
steps['llh'] = []
# Iterate over the cartesian product, and set fixed parameter to value
for pos in product(*steplist):
pos_dict = dict(list(pos))
print "Running at params-pos dict: ",pos_dict
for k,v in pos_dict.items():
hypo_params[k]['value'] = v
steps[k].append(v)
with Timer() as t:
llh_data = find_opt_bfgs(asimov_data_set,template_maker,hypo_params,
minimizer_settings,args.save_steps,
normal_hierarchy=hypo_normal)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
steps['llh'].append(llh_data['llh'][-1])
#Store the LLH data
results[data_tag][hypo_tag] = steps
logging.warn("FINISHED. Saving to file: %s"%args.outfile)
to_json(results,args.outfile)
